Performance of an asset, for example, a wind turbine, can depend, to a high degree, on software by which it is being controlled. New software or a software update can be used to improve performance of assets. However, installing new software or a software update can produce varying performance results depending on ambient conditions, asset model, location, and other factors. Thus, in some cases, an update can result in a significant increase in production while, in other cases, an update can result in no significant impact. Accurate calculation of the performance increase can be of importance, for example, when update pricing models are outcome-based. For, example, the price of the update can be proportional to the delivered benefit. To analyze effects of an update on performance, performance validations can be carried out.
One approach to performance validation includes comparing operational data of an asset under updated software to historical operational data used as baseline data. Operational data for the updated software can be collected for a certain period (for example, two weeks) and compared to the baseline data. However, because ambient conditions for the two sets of data can vary significantly (due to comparing data collected during different time periods), no meaningful comparison can be possible. For example, a rain can increase performance of a wind turbine due to the rain cleaning the blades of the wind turbine. Thus, the validation results can be uncertain. Additionally, this approach can involve a significant manual effort.
Another approach to performance validation includes toggling between updated and baseline software (a previous version of software) for a period of time. By toggling the asset back and forth between the two sets of settings, performance analysis can be conducted for two sets of data collected under similar ambient conditions. The toggling can be performed at predetermined times, for example every 20 minutes. This approach allows reducing influence of changes in ambient conditions on validation results. However, data collected during a period of time (for example, two weeks) may not represent all-year operation of the asset. Ambient conditions can vary significantly with a season change. Conducting performance validations during the entire year can resolve this issue, but will potentially result in losing half of the benefit of the update.